引用本文: | 邹 涛,孙宏伟,田新广,等.网络入侵检测系统中的数据缩减技术.[J].国防科技大学学报,2003,25(6):16-20.[点击复制] |
ZOU Tao,SUN Hongwei,TIAN Xinguang,et al.Data Reduction in Network Based on the Intrusion Detection System[J].Journal of National University of Defense Technology,2003,25(6):16-20[点击复制] |
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网络入侵检测系统中的数据缩减技术 |
邹 涛, 孙宏伟, 田新广, 张尔扬 |
(国防科技大学 电子科学与工程学院,湖南 长沙 410073)
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摘要: |
在进行事件分析之前,网络入侵检测系统首先要面对数据缩减的问题。以ANIDS为背景,分析了两种重要的数据缩减技术:相关特征子集选择和特征再构造。提出了一种基于Wrapper方法的最优特征子集选取算法SRRW。在考虑学习算法偏置的情况下,通过识别强相关特征并引入约束,能够更快地搜索并获得最优的相关特征子集。从特征再构造角度出发实现数据缩减,并通过因子负荷量矩阵分析了原始特征之间的相关性。 |
关键词: 网络入侵检测 数据缩减 相关特征选取 主成分分析 |
DOI: |
投稿日期:2003-05-13 |
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Data Reduction in Network Based on the Intrusion Detection System |
ZOU Tao, SUN Hongwei, TIAN Xinguang, ZHANG Eryang |
(College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China)
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Abstract: |
NIDSs deal with the problem of data reduction before analyzing the events. Two important measures used in ANIDS are proposed: FSS and new feature construction. A novel algorithm named SRRW is put forward first, which can produce OFS by recognizing all strongly relevant features and restrict them in searching process. A feature construction method is used to get the OFS. The correlations between the original features can be analyzed by factor loading matrix. |
Keywords: NIDS data reduction relevant feature selection PCA |
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